url = "https://www.nytimes.com/elections/results/texas-senate"
nytimes_data = read_html(url, col_types = "ccdd")
nytimes_data## {xml_document}
## <html lang="en" itemscope="" xmlns:og="http://opengraphprotocol.org/schema/" itemtype="http://schema.org/NewsArticle">
## [1] <head>\n<title>Texas Senate Election Results: Beto O’Rourke vs. Ted ...
## [2] <body class="eln-race-page eln-2018-11-06 eln-forecast">\n<script ty ...nytimes_data %>%
html_nodes(css = "table")## {xml_nodeset (2)}
## [1] <table class="eln-table eln-results-table">\n<thead><tr>\n<th class= ...
## [2] <table class="eln-table eln-county-table">\n<thead><tr>\n<th class=" ...table_county = (nytimes_data %>% html_nodes(css = "table")) %>%
.[[2]] %>%
html_table() %>%
slice(1:(n() - 1)) %>%
select(-Rpt.) %>%
janitor::clean_names()#remove the commas in the numbers first
table_county$cruz <- gsub(",","",table_county$cruz)
table_county$o_rourke <- gsub(",","",table_county$o_rourke)
table_county$dikeman <- gsub(",","",table_county$dikeman)
table_county =
table_county %>%
mutate(cruz = as.numeric(cruz), o_rourke = as.numeric(o_rourke), dikeman = as.numeric(dikeman))
table_county$county = str_replace(table_county$county,"La Vaca", "Lavaca")
table_county$county = str_replace(table_county$county,"De Witt", "DeWitt")
#county names misspelled (was causing issues with merging later on)
table_county_long = gather(table_county, key = candidate, value = votes, cruz:dikeman)for_GIS= table_county %>%
mutate(cruz_orour_rat = cruz/o_rourke,
status = as.numeric(cruz_orour_rat>=1),
winner = ifelse(status==1, "Ted Cruz", "Beto O'Rourke"))Cleaning data so as to merge with GIS…
texas_counties = counties(state = "48",year = "2017") #Texas fips code is 48, using most recent census data, 2017##
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|== | 4%
|
|=== | 4%
|
|=== | 5%
|
|==== | 5%
|
|==== | 6%
|
|==== | 7%
|
|===== | 7%
|
|===== | 8%
|
|====== | 8%
|
|====== | 9%
|
|====== | 10%
|
|======= | 10%
|
|======= | 11%
|
|======= | 12%
|
|======== | 12%
|
|======== | 13%
|
|========= | 13%
|
|========= | 14%
|
|========= | 15%
|
|========== | 15%
|
|========== | 16%
|
|=========== | 16%
|
|=========== | 17%
|
|=========== | 18%
|
|============ | 18%
|
|============ | 19%
|
|============= | 19%
|
|============= | 20%
|
|============= | 21%
|
|============== | 21%
|
|============== | 22%
|
|=============== | 22%
|
|=============== | 23%
|
|=============== | 24%
|
|================ | 24%
|
|================ | 25%
|
|================= | 25%
|
|================= | 26%
|
|================= | 27%
|
|================== | 27%
|
|================== | 28%
|
|=================== | 28%
|
|=================== | 29%
|
|=================== | 30%
|
|==================== | 30%
|
|==================== | 31%
|
|==================== | 32%
|
|===================== | 32%
|
|===================== | 33%
|
|====================== | 33%
|
|====================== | 34%
|
|====================== | 35%
|
|======================= | 35%
|
|======================= | 36%
|
|======================== | 36%
|
|======================== | 37%
|
|======================== | 38%
|
|========================= | 38%
|
|========================= | 39%
|
|========================== | 39%
|
|========================== | 40%
|
|========================== | 41%
|
|=========================== | 41%
|
|=========================== | 42%
|
|============================ | 42%
|
|============================ | 43%
|
|============================ | 44%
|
|============================= | 44%
|
|============================= | 45%
|
|============================== | 45%
|
|============================== | 46%
|
|============================== | 47%
|
|=============================== | 47%
|
|=============================== | 48%
|
|================================ | 48%
|
|================================ | 49%
|
|================================ | 50%
|
|================================= | 50%
|
|================================= | 51%
|
|================================= | 52%
|
|================================== | 52%
|
|================================== | 53%
|
|=================================== | 53%
|
|=================================== | 54%
|
|=================================== | 55%
|
|==================================== | 55%
|
|==================================== | 56%
|
|===================================== | 56%
|
|===================================== | 57%
|
|===================================== | 58%
|
|====================================== | 58%
|
|====================================== | 59%
|
|======================================= | 59%
|
|======================================= | 60%
|
|======================================= | 61%
|
|======================================== | 61%
|
|======================================== | 62%
|
|========================================= | 62%
|
|========================================= | 63%
|
|========================================= | 64%
|
|========================================== | 64%
|
|========================================== | 65%
|
|=========================================== | 65%
|
|=========================================== | 66%
|
|=========================================== | 67%
|
|============================================ | 67%
|
|============================================ | 68%
|
|============================================= | 68%
|
|============================================= | 69%
|
|============================================= | 70%
|
|============================================== | 70%
|
|============================================== | 71%
|
|============================================== | 72%
|
|=============================================== | 72%
|
|=============================================== | 73%
|
|================================================ | 73%
|
|================================================ | 74%
|
|================================================ | 75%
|
|================================================= | 75%
|
|================================================= | 76%
|
|================================================== | 76%
|
|================================================== | 77%
|
|================================================== | 78%
|
|=================================================== | 78%
|
|=================================================== | 79%
|
|==================================================== | 79%
|
|==================================================== | 80%
|
|==================================================== | 81%
|
|===================================================== | 81%
|
|===================================================== | 82%
|
|====================================================== | 82%
|
|====================================================== | 83%
|
|====================================================== | 84%
|
|======================================================= | 84%
|
|======================================================= | 85%
|
|======================================================== | 85%
|
|======================================================== | 86%
|
|======================================================== | 87%
|
|========================================================= | 87%
|
|========================================================= | 88%
|
|========================================================== | 88%
|
|========================================================== | 89%
|
|========================================================== | 90%
|
|=========================================================== | 90%
|
|=========================================================== | 91%
|
|=========================================================== | 92%
|
|============================================================ | 92%
|
|============================================================ | 93%
|
|============================================================= | 93%
|
|============================================================= | 94%
|
|============================================================= | 95%
|
|============================================================== | 95%
|
|============================================================== | 96%
|
|=============================================================== | 96%
|
|=============================================================== | 97%
|
|=============================================================== | 98%
|
|================================================================ | 98%
|
|================================================================ | 99%
|
|=================================================================| 99%
|
|=================================================================| 100%tx_midterms = geo_join(texas_counties, for_GIS, "NAME", "county")pal = colorFactor(c("blue", "red"), tx_midterms$status)
popup = paste(sep = "<br/>",
paste0("<b>Winner: </b>", tx_midterms$winner),
paste0("<b>County: </b>", tx_midterms$county),
paste0("<b>Votes for Ted Cruz: </b>", tx_midterms$cruz),
paste0("<b>Votes for Beto O'Rourke: </b>", tx_midterms$o_rourke),
paste0("<b>Votes for Neal Dikeman: </b>", tx_midterms$dikeman))leaflet(tx_midterms) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(fillColor= ~pal(status),
color = "black",
popup = popup,
fillOpacity = 0.5,
weight = 0.5) %>%
addLegend(position = "bottomleft",
colors = c("blue", "red"),
labels = c("Democrat", "Republican"),
opacity = 1,
title = "2018 Texas Senate Election Results")